Overview

Dataset statistics

Number of variables16
Number of observations940
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory117.6 KiB
Average record size in memory128.1 B

Variable types

Numeric14
Categorical2

Alerts

Calories is highly overall correlated with TotalDistance and 3 other fieldsHigh correlation
Date is highly overall correlated with DayHigh correlation
Day is highly overall correlated with DateHigh correlation
FairlyActiveMinutes is highly overall correlated with ModeratelyActiveDistance and 5 other fieldsHigh correlation
LightActiveDistance is highly overall correlated with LightlyActiveMinutes and 3 other fieldsHigh correlation
LightlyActiveMinutes is highly overall correlated with LightActiveDistance and 3 other fieldsHigh correlation
ModeratelyActiveDistance is highly overall correlated with FairlyActiveMinutes and 5 other fieldsHigh correlation
TotalDistance is highly overall correlated with Calories and 8 other fieldsHigh correlation
TotalSteps is highly overall correlated with Calories and 8 other fieldsHigh correlation
TrackerDistance is highly overall correlated with Calories and 8 other fieldsHigh correlation
VeryActiveDistance is highly overall correlated with FairlyActiveMinutes and 5 other fieldsHigh correlation
VeryActiveMinutes is highly overall correlated with Calories and 6 other fieldsHigh correlation
Date is uniformly distributedUniform
TotalSteps has 77 (8.2%) zerosZeros
TotalDistance has 78 (8.3%) zerosZeros
TrackerDistance has 78 (8.3%) zerosZeros
LoggedActivitiesDistance has 908 (96.6%) zerosZeros
VeryActiveDistance has 413 (43.9%) zerosZeros
ModeratelyActiveDistance has 386 (41.1%) zerosZeros
LightActiveDistance has 85 (9.0%) zerosZeros
SedentaryActiveDistance has 858 (91.3%) zerosZeros
VeryActiveMinutes has 409 (43.5%) zerosZeros
FairlyActiveMinutes has 384 (40.9%) zerosZeros
LightlyActiveMinutes has 84 (8.9%) zerosZeros

Reproduction

Analysis started2024-03-30 12:12:10.189766
Analysis finished2024-03-30 12:13:38.243366
Duration1 minute and 28.05 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Distinct33
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8554074 × 109
Minimum1.5039604 × 109
Maximum8.8776894 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:38.539344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.5039604 × 109
5-th percentile1.6245801 × 109
Q12.320127 × 109
median4.445115 × 109
Q36.9621811 × 109
95-th percentile8.7920097 × 109
Maximum8.8776894 × 109
Range7.373729 × 109
Interquartile range (IQR)4.6420541 × 109

Descriptive statistics

Standard deviation2.4248055 × 109
Coefficient of variation (CV)0.4994031
Kurtosis-1.2730307
Mean4.8554074 × 109
Median Absolute Deviation (MAD)2.418763 × 109
Skewness0.1771249
Sum4.5640829 × 1012
Variance5.8796816 × 1018
MonotonicityIncreasing
2024-03-30T12:13:39.251096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1503960366 31
 
3.3%
4319703577 31
 
3.3%
8583815059 31
 
3.3%
8378563200 31
 
3.3%
8053475328 31
 
3.3%
7086361926 31
 
3.3%
6962181067 31
 
3.3%
5553957443 31
 
3.3%
4702921684 31
 
3.3%
4558609924 31
 
3.3%
Other values (23) 630
67.0%
ValueCountFrequency (%)
1503960366 31
3.3%
1624580081 31
3.3%
1644430081 30
3.2%
1844505072 31
3.3%
1927972279 31
3.3%
2022484408 31
3.3%
2026352035 31
3.3%
2320127002 31
3.3%
2347167796 18
1.9%
2873212765 31
3.3%
ValueCountFrequency (%)
8877689391 31
3.3%
8792009665 29
3.1%
8583815059 31
3.3%
8378563200 31
3.3%
8253242879 19
2.0%
8053475328 31
3.3%
7086361926 31
3.3%
7007744171 26
2.8%
6962181067 31
3.3%
6775888955 26
2.8%

Date
Categorical

HIGH CORRELATION  UNIFORM 

Distinct31
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
2016-04-12
 
33
2016-04-15
 
33
2016-04-13
 
33
2016-04-14
 
33
2016-04-22
 
32
Other values (26)
776 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters9400
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016-04-12
2nd row2016-04-13
3rd row2016-04-14
4th row2016-04-15
5th row2016-04-16

Common Values

ValueCountFrequency (%)
2016-04-12 33
 
3.5%
2016-04-15 33
 
3.5%
2016-04-13 33
 
3.5%
2016-04-14 33
 
3.5%
2016-04-22 32
 
3.4%
2016-04-21 32
 
3.4%
2016-04-16 32
 
3.4%
2016-04-18 32
 
3.4%
2016-04-19 32
 
3.4%
2016-04-20 32
 
3.4%
Other values (21) 616
65.5%

Length

2024-03-30T12:13:39.511139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2016-04-12 33
 
3.5%
2016-04-13 33
 
3.5%
2016-04-14 33
 
3.5%
2016-04-15 33
 
3.5%
2016-04-27 32
 
3.4%
2016-04-24 32
 
3.4%
2016-04-23 32
 
3.4%
2016-04-28 32
 
3.4%
2016-04-26 32
 
3.4%
2016-04-25 32
 
3.4%
Other values (21) 616
65.5%

Most occurring characters

ValueCountFrequency (%)
0 2227
23.7%
- 1880
20.0%
2 1375
14.6%
1 1357
14.4%
6 1033
11.0%
4 705
 
7.5%
5 423
 
4.5%
3 125
 
1.3%
7 93
 
1.0%
8 91
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2227
23.7%
- 1880
20.0%
2 1375
14.6%
1 1357
14.4%
6 1033
11.0%
4 705
 
7.5%
5 423
 
4.5%
3 125
 
1.3%
7 93
 
1.0%
8 91
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2227
23.7%
- 1880
20.0%
2 1375
14.6%
1 1357
14.4%
6 1033
11.0%
4 705
 
7.5%
5 423
 
4.5%
3 125
 
1.3%
7 93
 
1.0%
8 91
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2227
23.7%
- 1880
20.0%
2 1375
14.6%
1 1357
14.4%
6 1033
11.0%
4 705
 
7.5%
5 423
 
4.5%
3 125
 
1.3%
7 93
 
1.0%
8 91
 
1.0%

Day
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size7.5 KiB
Tuesday
152 
Wednesday
150 
Thursday
147 
Friday
126 
Saturday
124 
Other values (2)
241 

Length

Max length9
Median length8
Mean length7.2170213
Min length6

Characters and Unicode

Total characters6784
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTuesday
2nd rowWednesday
3rd rowThursday
4th rowFriday
5th rowSaturday

Common Values

ValueCountFrequency (%)
Tuesday 152
16.2%
Wednesday 150
16.0%
Thursday 147
15.6%
Friday 126
13.4%
Saturday 124
13.2%
Sunday 121
12.9%
Monday 120
12.8%

Length

2024-03-30T12:13:39.768249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T12:13:40.093139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tuesday 152
16.2%
wednesday 150
16.0%
thursday 147
15.6%
friday 126
13.4%
saturday 124
13.2%
sunday 121
12.9%
monday 120
12.8%

Most occurring characters

ValueCountFrequency (%)
d 1090
16.1%
a 1064
15.7%
y 940
13.9%
u 544
8.0%
e 452
6.7%
s 449
6.6%
r 397
 
5.9%
n 391
 
5.8%
T 299
 
4.4%
S 245
 
3.6%
Other values (7) 913
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1090
16.1%
a 1064
15.7%
y 940
13.9%
u 544
8.0%
e 452
6.7%
s 449
6.6%
r 397
 
5.9%
n 391
 
5.8%
T 299
 
4.4%
S 245
 
3.6%
Other values (7) 913
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1090
16.1%
a 1064
15.7%
y 940
13.9%
u 544
8.0%
e 452
6.7%
s 449
6.6%
r 397
 
5.9%
n 391
 
5.8%
T 299
 
4.4%
S 245
 
3.6%
Other values (7) 913
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1090
16.1%
a 1064
15.7%
y 940
13.9%
u 544
8.0%
e 452
6.7%
s 449
6.6%
r 397
 
5.9%
n 391
 
5.8%
T 299
 
4.4%
S 245
 
3.6%
Other values (7) 913
13.5%

Calories
Real number (ℝ)

HIGH CORRELATION 

Distinct734
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2303.6096
Minimum0
Maximum4900
Zeros4
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:40.383233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1372.85
Q11828.5
median2134
Q32793.25
95-th percentile3654.25
Maximum4900
Range4900
Interquartile range (IQR)964.75

Descriptive statistics

Standard deviation718.16686
Coefficient of variation (CV)0.3117572
Kurtosis0.62502694
Mean2303.6096
Median Absolute Deviation (MAD)467
Skewness0.42245048
Sum2165393
Variance515763.64
MonotonicityNot monotonic
2024-03-30T12:13:40.671937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1980 13
 
1.4%
2063 11
 
1.2%
1841 9
 
1.0%
1688 9
 
1.0%
1347 8
 
0.9%
2225 4
 
0.4%
1819 4
 
0.4%
2044 4
 
0.4%
1922 4
 
0.4%
0 4
 
0.4%
Other values (724) 870
92.6%
ValueCountFrequency (%)
0 4
0.4%
52 1
 
0.1%
57 1
 
0.1%
120 1
 
0.1%
257 1
 
0.1%
403 1
 
0.1%
665 1
 
0.1%
741 1
 
0.1%
928 1
 
0.1%
1002 1
 
0.1%
ValueCountFrequency (%)
4900 1
0.1%
4552 1
0.1%
4547 1
0.1%
4546 1
0.1%
4501 1
0.1%
4398 1
0.1%
4392 1
0.1%
4274 1
0.1%
4236 1
0.1%
4163 1
0.1%

TotalSteps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct842
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7637.9106
Minimum0
Maximum36019
Zeros77
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:40.968340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13789.75
median7405.5
Q310727
95-th percentile15485.1
Maximum36019
Range36019
Interquartile range (IQR)6937.25

Descriptive statistics

Standard deviation5087.1507
Coefficient of variation (CV)0.66603957
Kurtosis1.1691112
Mean7637.9106
Median Absolute Deviation (MAD)3446.5
Skewness0.65289494
Sum7179636
Variance25879103
MonotonicityNot monotonic
2024-03-30T12:13:41.259656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77
 
8.2%
244 2
 
0.2%
6708 2
 
0.2%
9167 2
 
0.2%
6175 2
 
0.2%
10538 2
 
0.2%
1510 2
 
0.2%
8538 2
 
0.2%
7937 2
 
0.2%
4363 2
 
0.2%
Other values (832) 845
89.9%
ValueCountFrequency (%)
0 77
8.2%
4 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
16 1
 
0.1%
17 1
 
0.1%
29 1
 
0.1%
31 1
 
0.1%
42 1
 
0.1%
44 1
 
0.1%
ValueCountFrequency (%)
36019 1
0.1%
29326 1
0.1%
27745 1
0.1%
23629 1
0.1%
23186 1
0.1%
22988 1
0.1%
22770 1
0.1%
22359 1
0.1%
22244 1
0.1%
22026 1
0.1%

TotalDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct615
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4897021
Minimum0
Maximum28.030001
Zeros78
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:41.554060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.6199999
median5.2449999
Q37.7125
95-th percentile11.6565
Maximum28.030001
Range28.030001
Interquartile range (IQR)5.0925001

Descriptive statistics

Standard deviation3.9246059
Coefficient of variation (CV)0.71490325
Kurtosis3.1130184
Mean5.4897021
Median Absolute Deviation (MAD)2.5600001
Skewness1.1262736
Sum5160.32
Variance15.402532
MonotonicityNot monotonic
2024-03-30T12:13:41.843483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
8.3%
2.599999905 5
 
0.5%
0.009999999776 5
 
0.5%
3.910000086 4
 
0.4%
4.949999809 4
 
0.4%
1.789999962 4
 
0.4%
4.329999924 4
 
0.4%
2.680000067 4
 
0.4%
3.50999999 4
 
0.4%
4.900000095 4
 
0.4%
Other values (605) 824
87.7%
ValueCountFrequency (%)
0 78
8.3%
0.009999999776 5
 
0.5%
0.01999999955 1
 
0.1%
0.02999999933 2
 
0.2%
0.03999999911 1
 
0.1%
0.07999999821 1
 
0.1%
0.09000000358 1
 
0.1%
0.1000000015 1
 
0.1%
0.1099999994 1
 
0.1%
0.1299999952 1
 
0.1%
ValueCountFrequency (%)
28.03000069 1
0.1%
26.71999931 1
0.1%
25.29000092 1
0.1%
20.64999962 1
0.1%
20.39999962 1
0.1%
19.55999947 1
0.1%
19.34000015 1
0.1%
18.97999954 1
0.1%
18.25 1
0.1%
18.11000061 1
0.1%

TrackerDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct613
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4753511
Minimum0
Maximum28.030001
Zeros78
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:42.136079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.6199999
median5.2449999
Q37.71
95-th percentile11.6565
Maximum28.030001
Range28.030001
Interquartile range (IQR)5.0900002

Descriptive statistics

Standard deviation3.9072759
Coefficient of variation (CV)0.71361195
Kurtosis3.2038891
Mean5.4753511
Median Absolute Deviation (MAD)2.5550003
Skewness1.1345496
Sum5146.83
Variance15.266805
MonotonicityNot monotonic
2024-03-30T12:13:42.414231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
8.3%
2.599999905 5
 
0.5%
0.009999999776 5
 
0.5%
3.910000086 4
 
0.4%
2.680000067 4
 
0.4%
1.789999962 4
 
0.4%
4.329999924 4
 
0.4%
4.949999809 4
 
0.4%
3.50999999 4
 
0.4%
8.739999771 4
 
0.4%
Other values (603) 824
87.7%
ValueCountFrequency (%)
0 78
8.3%
0.009999999776 5
 
0.5%
0.01999999955 1
 
0.1%
0.02999999933 2
 
0.2%
0.03999999911 1
 
0.1%
0.07999999821 1
 
0.1%
0.09000000358 1
 
0.1%
0.1000000015 1
 
0.1%
0.1099999994 1
 
0.1%
0.1299999952 1
 
0.1%
ValueCountFrequency (%)
28.03000069 1
0.1%
26.71999931 1
0.1%
25.29000092 1
0.1%
20.64999962 1
0.1%
20.39999962 1
0.1%
19.55999947 1
0.1%
19.34000015 1
0.1%
18.97999954 1
0.1%
18.25 1
0.1%
18.11000061 1
0.1%

LoggedActivitiesDistance
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10817094
Minimum0
Maximum4.942142
Zeros908
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:42.667562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.942142
Range4.942142
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61989652
Coefficient of variation (CV)5.7307121
Kurtosis41.295941
Mean0.10817094
Median Absolute Deviation (MAD)0
Skewness6.2974404
Sum101.68068
Variance0.38427169
MonotonicityNot monotonic
2024-03-30T12:13:42.942092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 908
96.6%
2.092147112 9
 
1.0%
2.253081083 7
 
0.7%
4.081692219 1
 
0.1%
4.861792088 1
 
0.1%
4.878232002 1
 
0.1%
4.912367821 1
 
0.1%
2.832325935 1
 
0.1%
4.911146164 1
 
0.1%
4.885604858 1
 
0.1%
Other values (9) 9
 
1.0%
ValueCountFrequency (%)
0 908
96.6%
1.959596038 1
 
0.1%
2.092147112 9
 
1.0%
2.253081083 7
 
0.7%
2.785175085 1
 
0.1%
2.832325935 1
 
0.1%
3.167821884 1
 
0.1%
3.285414934 1
 
0.1%
4.081692219 1
 
0.1%
4.851306915 1
 
0.1%
ValueCountFrequency (%)
4.94214201 1
0.1%
4.930550098 1
0.1%
4.924840927 1
0.1%
4.912367821 1
0.1%
4.911146164 1
0.1%
4.885604858 1
0.1%
4.878232002 1
0.1%
4.869782925 1
0.1%
4.861792088 1
0.1%
4.851306915 1
0.1%

VeryActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct333
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5026809
Minimum0
Maximum21.92
Zeros413
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:43.436128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.20999999
Q32.0524999
95-th percentile6.4030001
Maximum21.92
Range21.92
Interquartile range (IQR)2.0524999

Descriptive statistics

Standard deviation2.6589412
Coefficient of variation (CV)1.769465
Kurtosis11.910951
Mean1.5026809
Median Absolute Deviation (MAD)0.20999999
Skewness2.99617
Sum1412.52
Variance7.0699681
MonotonicityNot monotonic
2024-03-30T12:13:47.258971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
43.9%
0.0700000003 9
 
1.0%
0.05999999866 6
 
0.6%
0.1400000006 5
 
0.5%
0.3300000131 5
 
0.5%
0.3400000036 4
 
0.4%
1.059999943 4
 
0.4%
0.3600000143 4
 
0.4%
1.00999999 4
 
0.4%
2.789999962 4
 
0.4%
Other values (323) 482
51.3%
ValueCountFrequency (%)
0 413
43.9%
0.01999999955 2
 
0.2%
0.03999999911 1
 
0.1%
0.05000000075 3
 
0.3%
0.05999999866 6
 
0.6%
0.0700000003 9
 
1.0%
0.07999999821 4
 
0.4%
0.09000000358 1
 
0.1%
0.1099999994 3
 
0.3%
0.1199999973 3
 
0.3%
ValueCountFrequency (%)
21.92000008 1
0.1%
21.65999985 1
0.1%
13.39999962 1
0.1%
13.26000023 1
0.1%
13.23999977 1
0.1%
13.22000027 1
0.1%
13.13000011 1
0.1%
13.06999969 1
0.1%
12.78999996 1
0.1%
12.53999996 1
0.1%

ModeratelyActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct211
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56754255
Minimum0
Maximum6.48
Zeros386
Zeros (%)41.1%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:48.020391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.23999999
Q30.80000001
95-th percentile2.1300001
Maximum6.48
Range6.48
Interquartile range (IQR)0.80000001

Descriptive statistics

Standard deviation0.88358032
Coefficient of variation (CV)1.556853
Kurtosis10.125629
Mean0.56754255
Median Absolute Deviation (MAD)0.23999999
Skewness2.7711936
Sum533.49
Variance0.78071418
MonotonicityNot monotonic
2024-03-30T12:13:48.473742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 386
41.1%
0.200000003 9
 
1.0%
0.2800000012 9
 
1.0%
0.400000006 9
 
1.0%
0.25 8
 
0.9%
0.3100000024 8
 
0.9%
0.9300000072 8
 
0.9%
0.4199999869 8
 
0.9%
0.2700000107 7
 
0.7%
0.5699999928 7
 
0.7%
Other values (201) 481
51.2%
ValueCountFrequency (%)
0 386
41.1%
0.009999999776 1
 
0.1%
0.01999999955 1
 
0.1%
0.02999999933 3
 
0.3%
0.03999999911 3
 
0.3%
0.05000000075 3
 
0.3%
0.05999999866 3
 
0.3%
0.0700000003 2
 
0.2%
0.07999999821 4
 
0.4%
0.09000000358 2
 
0.2%
ValueCountFrequency (%)
6.480000019 1
 
0.1%
6.210000038 1
 
0.1%
5.599999905 1
 
0.1%
5.400000095 1
 
0.1%
5.239999771 1
 
0.1%
5.119999886 1
 
0.1%
4.579999924 1
 
0.1%
4.559999943 1
 
0.1%
4.349999905 1
 
0.1%
4.21999979 3
0.3%

LightActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct491
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3408191
Minimum0
Maximum10.71
Zeros85
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:48.786078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.945
median3.3649999
Q34.7825001
95-th percentile6.462
Maximum10.71
Range10.71
Interquartile range (IQR)2.8375001

Descriptive statistics

Standard deviation2.0406554
Coefficient of variation (CV)0.61082486
Kurtosis-0.18030027
Mean3.3408191
Median Absolute Deviation (MAD)1.4200002
Skewness0.18224747
Sum3140.37
Variance4.1642744
MonotonicityNot monotonic
2024-03-30T12:13:49.382111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85
 
9.0%
4.179999828 6
 
0.6%
3.170000076 6
 
0.6%
4.880000114 6
 
0.6%
3.230000019 6
 
0.6%
3.940000057 5
 
0.5%
3.25999999 5
 
0.5%
0.009999999776 5
 
0.5%
4.460000038 5
 
0.5%
5.409999847 5
 
0.5%
Other values (481) 806
85.7%
ValueCountFrequency (%)
0 85
9.0%
0.009999999776 5
 
0.5%
0.01999999955 1
 
0.1%
0.02999999933 3
 
0.3%
0.03999999911 1
 
0.1%
0.05999999866 1
 
0.1%
0.09000000358 1
 
0.1%
0.1000000015 1
 
0.1%
0.1099999994 1
 
0.1%
0.1299999952 2
 
0.2%
ValueCountFrequency (%)
10.71000004 1
0.1%
10.56999969 1
0.1%
10.30000019 1
0.1%
9.479999542 1
0.1%
9.460000038 1
0.1%
8.970000267 1
0.1%
8.789999962 1
0.1%
8.680000305 1
0.1%
8.409999847 1
0.1%
8.270000458 1
0.1%

SedentaryActiveDistance
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001606383
Minimum0
Maximum0.11
Zeros858
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:50.095461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0099999998
Maximum0.11
Range0.11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0073461763
Coefficient of variation (CV)4.5731164
Kurtosis99.127446
Mean0.001606383
Median Absolute Deviation (MAD)0
Skewness8.5898992
Sum1.51
Variance5.3966306 × 10-5
MonotonicityNot monotonic
2024-03-30T12:13:50.502602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 858
91.3%
0.009999999776 50
 
5.3%
0.01999999955 21
 
2.2%
0.02999999933 4
 
0.4%
0.05000000075 3
 
0.3%
0.0700000003 1
 
0.1%
0.03999999911 1
 
0.1%
0.1099999994 1
 
0.1%
0.1000000015 1
 
0.1%
ValueCountFrequency (%)
0 858
91.3%
0.009999999776 50
 
5.3%
0.01999999955 21
 
2.2%
0.02999999933 4
 
0.4%
0.03999999911 1
 
0.1%
0.05000000075 3
 
0.3%
0.0700000003 1
 
0.1%
0.1000000015 1
 
0.1%
0.1099999994 1
 
0.1%
ValueCountFrequency (%)
0.1099999994 1
 
0.1%
0.1000000015 1
 
0.1%
0.0700000003 1
 
0.1%
0.05000000075 3
 
0.3%
0.03999999911 1
 
0.1%
0.02999999933 4
 
0.4%
0.01999999955 21
 
2.2%
0.009999999776 50
 
5.3%
0 858
91.3%

VeryActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct122
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.164894
Minimum0
Maximum210
Zeros409
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:51.347712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q332
95-th percentile93.05
Maximum210
Range210
Interquartile range (IQR)32

Descriptive statistics

Standard deviation32.844803
Coefficient of variation (CV)1.551853
Kurtosis5.7780701
Mean21.164894
Median Absolute Deviation (MAD)4
Skewness2.1761432
Sum19895
Variance1078.7811
MonotonicityNot monotonic
2024-03-30T12:13:52.203979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 409
43.5%
1 23
 
2.4%
2 18
 
1.9%
3 16
 
1.7%
8 15
 
1.6%
6 14
 
1.5%
11 14
 
1.5%
19 13
 
1.4%
5 13
 
1.4%
14 12
 
1.3%
Other values (112) 393
41.8%
ValueCountFrequency (%)
0 409
43.5%
1 23
 
2.4%
2 18
 
1.9%
3 16
 
1.7%
4 10
 
1.1%
5 13
 
1.4%
6 14
 
1.5%
7 11
 
1.2%
8 15
 
1.6%
9 7
 
0.7%
ValueCountFrequency (%)
210 1
0.1%
207 1
0.1%
200 1
0.1%
194 1
0.1%
186 1
0.1%
184 1
0.1%
137 1
0.1%
132 1
0.1%
129 1
0.1%
125 2
0.2%

FairlyActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.564894
Minimum0
Maximum143
Zeros384
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:52.534232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q319
95-th percentile51
Maximum143
Range143
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.987404
Coefficient of variation (CV)1.4734656
Kurtosis7.9957314
Mean13.564894
Median Absolute Deviation (MAD)6
Skewness2.479492
Sum12751
Variance399.49632
MonotonicityNot monotonic
2024-03-30T12:13:52.980290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
40.9%
8 36
 
3.8%
6 23
 
2.4%
5 23
 
2.4%
16 22
 
2.3%
7 20
 
2.1%
10 19
 
2.0%
9 19
 
2.0%
13 18
 
1.9%
11 18
 
1.9%
Other values (71) 358
38.1%
ValueCountFrequency (%)
0 384
40.9%
1 10
 
1.1%
2 8
 
0.9%
3 9
 
1.0%
4 14
 
1.5%
5 23
 
2.4%
6 23
 
2.4%
7 20
 
2.1%
8 36
 
3.8%
9 19
 
2.0%
ValueCountFrequency (%)
143 1
 
0.1%
125 1
 
0.1%
122 1
 
0.1%
116 1
 
0.1%
115 1
 
0.1%
113 1
 
0.1%
98 1
 
0.1%
96 1
 
0.1%
95 5
0.5%
94 1
 
0.1%

LightlyActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct335
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.81277
Minimum0
Maximum518
Zeros84
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:53.593103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1127
median199
Q3264
95-th percentile369.05
Maximum518
Range518
Interquartile range (IQR)137

Descriptive statistics

Standard deviation109.1747
Coefficient of variation (CV)0.56622132
Kurtosis-0.36011793
Mean192.81277
Median Absolute Deviation (MAD)69
Skewness-0.037929343
Sum181244
Variance11919.115
MonotonicityNot monotonic
2024-03-30T12:13:54.191783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84
 
8.9%
206 12
 
1.3%
258 10
 
1.1%
195 9
 
1.0%
214 8
 
0.9%
139 7
 
0.7%
238 7
 
0.7%
141 7
 
0.7%
199 7
 
0.7%
227 7
 
0.7%
Other values (325) 782
83.2%
ValueCountFrequency (%)
0 84
8.9%
1 3
 
0.3%
2 4
 
0.4%
3 3
 
0.3%
4 1
 
0.1%
9 3
 
0.3%
10 2
 
0.2%
11 1
 
0.1%
12 2
 
0.2%
15 1
 
0.1%
ValueCountFrequency (%)
518 1
0.1%
513 1
0.1%
512 1
0.1%
487 1
0.1%
480 1
0.1%
475 1
0.1%
461 1
0.1%
458 1
0.1%
448 1
0.1%
439 1
0.1%

SedentaryMinutes
Real number (ℝ)

Distinct549
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean991.21064
Minimum0
Maximum1440
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-30T12:13:54.941883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile536.7
Q1729.75
median1057.5
Q31229.5
95-th percentile1440
Maximum1440
Range1440
Interquartile range (IQR)499.75

Descriptive statistics

Standard deviation301.26744
Coefficient of variation (CV)0.30393887
Kurtosis-0.66595003
Mean991.21064
Median Absolute Deviation (MAD)261
Skewness-0.29449809
Sum931738
Variance90762.068
MonotonicityNot monotonic
2024-03-30T12:13:55.725636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440 79
 
8.4%
1182 7
 
0.7%
692 6
 
0.6%
1112 5
 
0.5%
1131 5
 
0.5%
1122 5
 
0.5%
1105 5
 
0.5%
709 5
 
0.5%
1119 5
 
0.5%
728 5
 
0.5%
Other values (539) 813
86.5%
ValueCountFrequency (%)
0 1
0.1%
2 1
0.1%
13 1
0.1%
48 1
0.1%
111 1
0.1%
125 1
0.1%
127 1
0.1%
218 1
0.1%
222 1
0.1%
241 1
0.1%
ValueCountFrequency (%)
1440 79
8.4%
1439 3
 
0.3%
1438 3
 
0.3%
1437 2
 
0.2%
1431 1
 
0.1%
1430 2
 
0.2%
1428 1
 
0.1%
1423 1
 
0.1%
1420 1
 
0.1%
1413 1
 
0.1%

Interactions

2024-03-30T12:13:28.041574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:10.869457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:15.723592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:20.538775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:25.343862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:33.532097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:39.490184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:44.364053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:55.810681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:02.499131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:08.575213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:12.485762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:17.847723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:21.677706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:29.302640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:11.121377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:15.989307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:20.803388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:26.047229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:34.094189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:39.759560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:44.937381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:56.596960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:02.981843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:08.977258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:12.741688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:18.216576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:21.942858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:30.062301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:11.367813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:16.244888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:21.055265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:27.261952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:34.705009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:39.999038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:45.641352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:57.122807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:03.532786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:09.376014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:12.989263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:18.540750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:22.195384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:30.818135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:11.667124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:16.582317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:21.326257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:27.626536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:35.186701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:40.264582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:46.527653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:57.774910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:03.993562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:09.675404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:13.244894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:18.808528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:22.456559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:31.393314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:12.100369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:16.912894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:21.576434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:28.038027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:35.576748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:40.499792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:47.455346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:58.274533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:04.393954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:09.938989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:13.490061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:19.086525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:22.709977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:32.077904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:12.476598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:17.258838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:21.831568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:28.398499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:35.849000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:40.740096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:48.385591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:58.570787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:04.801155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:10.188385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:13.744030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:19.340892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:23.154031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:32.813479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:12.917779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:17.635432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:22.080557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:28.722836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:36.131794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:40.979118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:49.242753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:58.869223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:05.060737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:10.440106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:14.043247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:19.591160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:23.626290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:33.300370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:13.274914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:17.927243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:22.339647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:29.171272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:36.429243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:41.259748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:49.806098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:59.145372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:05.431557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:10.690346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:15.229889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:19.834410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:24.108292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:33.793362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:13.840858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:18.340974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:22.603372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:29.441259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:36.752651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:41.617488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:50.404102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:59.604013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:05.711159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:10.952723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:15.557651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:20.106723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:24.600336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:34.173443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:14.220648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:18.651579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:22.861945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:29.868354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:37.181744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:41.906815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:51.079845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:59.957498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:06.001255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:11.197969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:15.946539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:20.358460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:24.916294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:34.598897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:14.561426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:18.999118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:23.113075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:30.414859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:37.712498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:42.292774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:51.972929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:00.477787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:06.322658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:11.449736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:16.354677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:20.612438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:25.483881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:34.896371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:14.895902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:19.373845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:23.395180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:31.202875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:38.317073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:42.685170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:53.151326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:00.878409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:06.605662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:11.699706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:16.741214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:20.877343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:26.138921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:35.361519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:15.192022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:19.784593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:23.913798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:32.115371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:38.860574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:43.378444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:54.067835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:01.417213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:06.923783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:11.976195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:17.120488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:21.165222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:26.676831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:35.861491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:15.451504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:20.174127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:24.575360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:32.983620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:39.238824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:43.889212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:12:54.870830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:01.939953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:08.223908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:12.223785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:17.527896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:21.414807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-30T12:13:27.292283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-03-30T12:13:56.165407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
CaloriesDateDayFairlyActiveMinutesIdLightActiveDistanceLightlyActiveMinutesLoggedActivitiesDistanceModeratelyActiveDistanceSedentaryActiveDistanceSedentaryMinutesTotalDistanceTotalStepsTrackerDistanceVeryActiveDistanceVeryActiveMinutes
Calories1.0000.1190.0260.4350.4290.4650.2860.2260.4030.010-0.1520.6170.5590.6170.4970.540
Date0.1191.0000.987-0.008-0.016-0.030-0.038-0.023-0.001-0.010-0.070-0.038-0.042-0.037-0.023-0.024
Day0.0260.9871.0000.010-0.008-0.014-0.0270.0250.010-0.017-0.0130.0210.0170.0200.0250.015
FairlyActiveMinutes0.435-0.0080.0101.0000.1250.3450.2320.1330.980-0.103-0.3140.6850.6890.6860.7430.746
Id0.429-0.016-0.0080.1251.0000.030-0.0840.2100.111-0.114-0.0640.1990.1580.1970.2230.251
LightActiveDistance0.465-0.030-0.0140.3450.0301.0000.8780.1390.3610.142-0.4660.7150.7150.7140.2850.285
LightlyActiveMinutes0.286-0.038-0.0270.232-0.0840.8781.0000.0570.2440.194-0.4800.5590.5810.5580.1580.152
LoggedActivitiesDistance0.226-0.0230.0250.1330.2100.1390.0571.0000.1570.010-0.0870.2030.1800.1930.2260.265
ModeratelyActiveDistance0.403-0.0010.0100.9800.1110.3610.2440.1571.000-0.096-0.3080.7010.7040.7010.7490.734
SedentaryActiveDistance0.010-0.010-0.017-0.103-0.1140.1420.1940.010-0.0961.0000.0960.0130.0150.011-0.064-0.057
SedentaryMinutes-0.152-0.070-0.013-0.314-0.064-0.466-0.480-0.087-0.3080.0961.000-0.414-0.428-0.415-0.235-0.241
TotalDistance0.617-0.0380.0210.6850.1990.7150.5590.2030.7010.013-0.4141.0000.9921.0000.7760.752
TotalSteps0.559-0.0420.0170.6890.1580.7150.5810.1800.7040.015-0.4280.9921.0000.9920.7700.749
TrackerDistance0.617-0.0370.0200.6860.1970.7140.5580.1930.7010.011-0.4151.0000.9921.0000.7750.751
VeryActiveDistance0.497-0.0230.0250.7430.2230.2850.1580.2260.749-0.064-0.2350.7760.7700.7751.0000.970
VeryActiveMinutes0.540-0.0240.0150.7460.2510.2850.1520.2650.734-0.057-0.2410.7520.7490.7510.9701.000

Missing values

2024-03-30T12:13:36.516083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T12:13:37.731642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdDateDayCaloriesTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutes
015039603662016-04-12Tuesday1985131628.508.500.01.880.556.060.02513328728
115039603662016-04-13Wednesday1797107356.976.970.01.570.694.710.02119217776
215039603662016-04-14Thursday1776104606.746.740.02.440.403.910.030111811218
315039603662016-04-15Friday174597626.286.280.02.141.262.830.02934209726
415039603662016-04-16Saturday1863126698.168.160.02.710.415.040.03610221773
515039603662016-04-17Sunday172897056.486.480.03.190.782.510.03820164539
615039603662016-04-18Monday1921130198.598.590.03.250.644.710.042162331149
715039603662016-04-19Tuesday2035155069.889.880.03.531.325.030.05031264775
815039603662016-04-20Wednesday1786105446.686.680.01.960.484.240.02812205818
915039603662016-04-21Thursday177598196.346.340.01.340.354.650.0198211838
IdDateDayCaloriesTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutes
93088776893912016-05-03Tuesday2817108188.2100008.2100000.01.390.106.670.011932291189
93188776893912016-05-04Wednesday34771819316.29999916.2999990.010.420.315.530.006682121154
93288776893912016-05-05Thursday30521405510.67000010.6700000.05.460.824.370.0067151881170
93388776893912016-05-06Friday40152172719.34000019.3400000.012.790.296.160.0096172321095
93488776893912016-05-07Saturday4142123328.1300008.1300000.00.080.966.990.00105282711036
93588776893912016-05-08Sunday2847106868.1100008.1100000.01.080.206.800.001742451174
93688776893912016-05-09Monday37102022618.25000018.2500000.011.100.806.240.0573192171131
93788776893912016-05-10Tuesday2832107338.1500008.1500000.01.350.466.280.0018112241187
93888776893912016-05-11Wednesday38322142019.55999919.5599990.013.220.415.890.0088122131127
93988776893912016-05-12Thursday184980646.1200006.1200000.01.820.044.250.00231137770